Akbuğday, BurakBozbas, O. A.Cura, O.K.Pehlivan, SudeAkan, Aydın2023-12-262023-12-26202397894645936002219-5491https://doi.org/10.23919/EUSIPCO58844.2023.10289818https://hdl.handle.net/20.500.14365/501931st European Signal Processing Conference, EUSIPCO 2023 -- 4 September 2023 through 8 September 2023 -- 194070Attention deficit hyperactivity disorder (ADHD) is a mental disorder that affects the behavior of the persons, and usually onsets in childhood. ADHD generally causes impulsivity, hyperactivity, and inattention which impairs day-to-day life even in the adulthood if left undiagnosed and untreated. Although various guidelines for diagnosis of ADHD exist, a universally accepted objective diagnostic procedure is not established. Since current diagnosis of ADHD heavily relies on the expertise of healthcare providers, an EEG Topographic Feature Map (EEG-FM) based method is proposed in this study which aims to objectively diagnose ADHD. 6 different features extracted from EEG recordings acquired from 33 participants, 15 ADHD patients and 18 control subjects, converted into EEG-FM images and fed into a convolutional neural network (CNN) based classifier. Results indicate that the proposed method can accurately classify ADHD patients with up to 99% accuracy, precision, and recall. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.eninfo:eu-repo/semantics/closedAccessAttention Deficit Hyperactivity Disorder (ADHD) detectionCNNdeep learningEEG feature mapsDeep learningDiagnosisDiseasesFeature extractionSignal processing'currentAttention deficit hyperactivity disorderAttention deficit hyperactivity disorder detectionConvolutional neural networkDeep learningDiagnostic procedureEEG feature mapFeature mapMental disordersTopographic featuresConvolutional neural networksConference Object10.23919/EUSIPCO58844.2023.102898182-s2.0-85178342858